AI Bites: The Academic Series

The ultimate high-yield visual recap of Stanford’s CS224N (Natural Language Processing with Deep Learning)! In just 20 minutes, we cover the full 15-module arc of the course—from the birth of word vec...

The grand finale of CS224N! In this final NotebookLM Video Short, we take a visual look at the "David vs. Goliath" revolution happening in AI reasoning.Key Topics:Smart Scaling vs. Brute-For...

A bite-sized, visual breakdown of CS224N's guest lecture on Multimodal Deep Learning! In this NotebookLM Video Short, we look at how models fake visual intelligence, and the craziest new frontier in A...

What happens when an AI can see, hear, and even smell? In this episode, featuring insights from former Meta FAIR and Hugging Face researcher Douwe Kiela, we break out of the text-only box. We explore ...

A bite-sized, visual breakdown of CS224N Lecture 16! In this NotebookLM Video Short, we pull back the curtain on why models are mathematically forced to lie, and how AI is subtly homogenizing human th...

A bite-sized, visual breakdown of CS224N's guest lecture with Dr. Been Kim! In this NotebookLM Video Short, we look at the mathematical failure of our current interpretability tools and how resear...

We are stepping away from optimizer tricks to tackle the downstream social and cognitive impacts of language models. Featuring Professor Yejin Choi's lecture, we explore the mathematical inevitabi...

How do we communicate with an AI that thinks in alien, superhuman concepts? In this episode, featuring insights from Google Brain’s Dr. Been Kim, we explore the massive gap between what we think machi...

A bite-sized, visual breakdown of CS224N Lecture 14! In this new NotebookLM Video Short, we pull back the curtain on the invisible preprocessing layer of modern AI: Tokenization.Key Topics:The "S...

Language models do not actually read text—they read tokens. In this episode, we explore the invisible preprocessing layer that Andrej Karpathy says is "at the heart of much weirdness of LLMs.&quo...

Today, we are pushing the absolute limits of how Language Models generate text. We move beyond basic architecture to explore how engineers are making AI insanely fast, teaching models to recover from ...

How does a language model actually "think"? In this episode, we dive into the fascinating mechanics of AI reasoning. We move past basic text prediction to explore how modern models generate ...

We spend so much time building massive AI models, but how do we actually know if they are any good? In this episode, we tackle the multi-billion-dollar scientific bottleneck: evaluation. We explore wh...

Up until now, we’ve looked at Language Models as isolated brains trapped in a box. In this episode, we cross the threshold into the absolute bleeding edge of AI: giving models a search engine to brows...

We know how to build and align massive foundational models, but what if you don't have a $100 million supercomputer? In this episode, we tackle the practical wall of modern AI: compute costs. We e...

How do we turn a raw, chaotic text-predictor into a helpful, conversational AI assistant? In this episode, we dive into the massive pipeline of Post-training. We explore the transition from Instructio...

If the Transformer architecture gave us the engine for modern AI, this episode is all about the fuel. We are diving into the single most consequential paradigm shift in modern NLP: Pre-training. We ex...

Last week, we saw how RNNs struggled with the "Bottleneck Problem" and sequential processing. This week, we explore the architecture that solved it and changed natural language processing fo...

We are continuing our journey through Stanford's CS224N by exploring the absolute foundation of modern natural language processing. In this episode, we break down Language Models and Recurrent Neu...

We are looking under the hood of deep learning to understand the mathematical engine driving modern artificial intelligence: Backpropagation. In this episode, we break down how neural networks transit...

How do you teach a computer the actual meaning of a word? In this episode, we dive into the fundamental building block of modern NLP: Word Vectors. We break down how algorithms map words into a dimens...

Welcome to a brand new series! We are diving into Stanford's CS224N. To understand where AI is today, we first need to understand how we got here. In this episode, we trace the evolution of Natura...

Short on time? We’ve distilled the entire Stanford CME295 course into a single, high-energy video recap. This "Cram Session" takes you on a complete journey from the absolute basics of natur...

We have reached the end of Stanford's CME295! In this course finale, we zoom out to summarize the entire journey—from the underlying Transformer architecture to the massive engineering feat of tra...

If an AI can write a poem, code a website, and pass the bar exam, how do we actually measure its performance? This episode tackles the notoriously difficult science of LLM Evaluation. We look at why s...

What happens when an AI stops just answering questions and starts taking action? In this episode, we explore Agentic LLMs. We break down how language models are evolving from passive text-generators i...

Can a language model actually think, or is it just a sophisticated autocomplete? In this episode, we dive into the fascinating—and highly debated—topic of LLM reasoning. We explore how to unlock compl...

A raw, pre-trained base model isn't very useful—it just wants to endlessly complete sentences. This episode covers the crucial second step: Tuning. We look at how developers take a chaotic text-ge...

Building a massive language model from scratch requires an astronomical amount of data and compute. In this episode, we explore the "pre-training" phase. We break down the sheer scale of the...

What happens when you take the Transformer architecture and scale it up with massive amounts of compute and data? You get Large Language Models (LLMs). We wrap up Week 1 by connecting the base archite...

Now that we know why Transformers work, it’s time to look under the hood. In this episode, we strip the architecture down to its studs, exploring the specific structural components that allow these mo...

We are kicking off our brand new CME295 series by going back to the breakthrough that started the current AI era. If you want a clear, foundational understanding of what a Transformer actually is and ...

Short on time? We’ve distilled the entire Stanford CS25: Transformers United course into a single 10-minute video. This "Cram Session" covers the journey of the Transformer architecture from...

Generating a single image is hard; generating a coherent video means mastering the dimension of time. In our CS25 finale, we break down how Transformers are being engineered to understand motion, phys...

Can an AI learn the "physics" of the human body? In this episode, we dive into the concept of World Models in medicine. We explore how Transformers are moving beyond simply analyzing static ...

What happens when you treat the building blocks of life as a language? In this episode, we explore how the exact same Transformer architecture used for ChatGPT is being applied to biology. DNA, RNA, a...

We wrap up Week 1 by zooming out to the endgame of AI research: Artificial General Intelligence (AGI). What separates our current generative tools from true, human-level reasoning? We discuss the theo...

Description:Having covered the base architecture, we now look at how these models learn to behave. This episode explores Reinforcement Learning (RL) within the context of modern foundation models, foc...

We are kicking off a brand new course with the architecture that changed everything: CS25. In this episode, we break down the fundamental mechanics of Transformers. If you've ever wondered how mod...

Short on time? We’ve distilled the entire Duke University "Machine Learning Foundations for Product Managers" course into a single 10-minute recap. This is the ultimate PM "Cram Session...

What happens when your data doesn't fit neatly into a straight line? We move to Tree Models. This episode explores how algorithms can mimic human decision-making through a series of "If/Then&...

Sometimes the best solution is the simplest one. In this episode, we unpack Linear Models—the most interpretable and transparent tools in a Product Manager's AI toolkit. We break down how the math...

A model can have 99% accuracy and still fail your users. In this episode, we tackle Model Evaluation from the Product Manager's perspective. We bridge the gap between technical model metrics (what...

Building a model is about more than just data—it’s about a repeatable process. This episode walks through the lifecycle of a machine learning project, focusing on the strategic decisions a Product Man...

We kick off a new course from Duke University designed specifically for those leading AI products. In this episode, we strip away the code and focus on the core vocabulary and intuition every Product ...

Short on time? We’ve distilled the entire Stanford CS21SI: AI for Social Good course into a single 10-minute video. This "Cram Session" covers the journey from ethical frameworks to technica...

Description:We’ve reached the finish line for Stanford’s CS21SI! In our final episode, we move beyond passive observation and into the world of sequential decision-making. We explore how Reinforcement...

We’re exploring how machines "see" and how that vision can be used to restore human agency. In this installment of Stanford’s CS21SI, we move from the technical mechanics of Convolutional Ne...

Description:How does AI decide what is "true"? This week, we dive into Natural Language Processing (NLP). We trace the evolution from RNNs to Transformers and discuss the massive threat Larg...

We’re moving from basic ML into the technical engine of modern AI: Deep Learning. In this week of Stanford’s CS21SI, we explore how neural networks can model complex, non-linear realities—specifically...